Your Challenge

• With the ever-increasing demand from the business to make data-driven decisions, IT is struggling to ensure quality in the large volumes and variety of data.
• IT consistently hears that the reports that the business relies on are not accurate, and they are required to perform time-intensive and costly manual fixes to the organization’s data.
• Data users do not trust the data quality in your company. Business users bypass IT and establish their own data silos to manage quality. Without governance or consistency, data quality issues are propagated further within data silos.
• Data is not usable or useful to the business due to deficiencies in quality and processes, resulting in decisions being made based on intuition and weak analysis. Worse, these decisions can be based on the wrong data.
• With sub-optimal data quality, companies are unable to launch related data initiatives such as business intelligence, master data management, and big data. They need a clear understanding of business requirements and the implementation of data quality processes first.

Our Advice

Critical Insight

• Data quality means tolerance, not perfection. One-hundred percent pure quality data does not exist. Instead, think of data quality as a level of error that is tolerated by the business. Tolerance for data quality is unique to each business unit, and “good enough” for the business must be determined before embarking on costly and lengthy data quality repairs. If the data allows the business to operate at the desired level, don’t waste time fixing data that may not need to be fixed.
• Prevention is 10x cheaper than remediation. While going through this blueprint, you will learn how to address data quality issues at the root. Stop fixing data quality with band-aid solutions and start fixing it by healing it at the source of the problem. This will prevent costly and lengthy data quality repairs that require manual repairs. Address and resolve the problem at the point of ingestion and at process level to ensure going forward the data quality is up to standard.
• Data quality without data governance is equivalent to treating the symptoms but not curing the disease. To ensure that data quality fixes address the root causes of the problems, as well as to maintain that mindset and effort throughout the organization, data quality processes must have the appropriate oversight and governance. If data quality is not embedded into the enterprise data governance framework, data quality management will remain a band-aid fix.

Impact and Result

• Implement a set of data quality initiatives that are aligned with overall business objectives and aimed at addressing data practices and the data itself.
• Develop a prioritized data quality improvement project roadmap and long-term improvement strategy.
• Build related practices such as business intelligence and analytics with more confidence and less risk after achieving an appropriate level of data quality.